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Assessing urban service locations is a key issue within city planning, integral to promoting the well-being of citizens, and ensuring effective urban development. However, many current approaches emphasize spatial analysis focused solely on physical attributes, neglecting the equally vital social dimensions essential for enhancing inhabitants' comfort and quality of life. When social factors are considered, they tend to operate at smaller scales. This paper addresses this gap by prioritizing integrating social factors alongside spatial analysis at the community level. By employing space syntax theory, this study investigates urban service suitability in Hajjah, a Palestinian urban community, presenting a novel approach in the literature. The research identifies good spots for essential governmental facilities like health clinics and fire stations using axial map analysis. It also suggests reallocation for some schools. Additionally, it shows ways to improve the placement of community amenities, finding ideal park locations but suboptimal mosque placements. Commercial services also exhibit areas for enhancement including gas stations and shops. The insights from this research can offer policymakers and planners insights to create more efficient, equitable, and accessible cities. The research approach incorporates social behavior dynamics into spatial analysis, promoting inclusive urban planning. [ABSTRACT FROM AUTHOR]

This study focuses on the integration of System Dynamics (SD), Artificial Intelligence (AI) technology, and 3D Urban Information Modeling (CityGML) in the field of urban planning. It aims to optimize urban layout to cope with rapid urban growth, emphasizing resource allocation through a combination of greedy strategies and AI methods. The core contribution of this paper is to provide spatial allocation of feedback from simulation techniques i.e. SD using the application of genetic algorithms (GA) based on a greedy strategy. The methodology was implemented using a fictitious case study in Berlin, where new students' dorms are required according to the growth in the number of students. The SD tool is used to simulate the growth over 5 years and determine the number of new dorms required. A genetic algorithm is used to optimize the planning objectives of allocating 16 new dorms in 186 possible locations. The algorithm effectively handles the feedback from SD and applies complex multi-objective optimization problems, addressing the challenge of accommodating a growing student population. It proposes strategic dormitory allocations that balance factors such as cost efficiency, campus accessibility, and proximity to public transportation systems. We use CityGML data to simulate and predict future urban transformation, providing dynamic and realistic representations for urban planning decisions and updating the original city models. [ABSTRACT FROM AUTHOR]

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